TOTAL VOLUME:
$54.6b
24H VOL:
$283,259,638
OPEN INTEREST:
$1,235,549,422
505,521
Markets across
13,661
events
MATCHED EVENTS:
1,892
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Maine Black Bears vs. Vermont Catamounts (W)
Volume:
$72,107
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the women's college basketball game between Maine Black Bears and Vermont Catamounts scheduled for March 13, 2026 at 5:00 PM ET. Both platforms are settling on the same binary outcome: which team wins the game.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution logic contains a logical contradiction where both Maine winning and Vermont winning are stated to resolve to Yes, making the market fundamentally unresolvable and unable to differentiate outcomes.Hero Tip:
This is a critical data integrity failure on Kalshi. The platform's resolution rules do not permit a meaningful binary settlement. Contact Kalshi support immediately to clarify whether the second outcome should resolve to No. Avoid trading Kalshi until the contradiction is resolved.Critical Divergence Points:
- Polymarket: Resolves to winning team name (Maine Black Bears or Vermont Catamounts) based on final score including overtime. Postponement keeps market open; cancellation without makeup resolves 50-50. Source: NCAA.com.
- Kalshi: States both outcomes resolve to Yes: If Vermont wins resolves Yes, if Maine wins resolves Yes. This creates a logical impossibility where no outcome can resolve to No.
Our PredictionHero Resolution Divergence Alerts (RDA) are there to help users identify potential differences across platforms. They do not replace or supersede the official rules and description of any prediction market. Users are solely responsible for reviewing and understanding the applicable rules and resolution criteria before placing any trade or bet. If you notice a potential inconsistency, discrepancy, or error in an alert, please report it to our team so we can review and improve the accuracy of our data.
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